{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Getting Started with Qiskit\n",
    "\n",
    "Here, we provide an overview of working with Qiskit.  The fundamental package of Qiskit is Terra that provides the basic building blocks necessary to program quantum computers. The fundamental unit of Qiskit is the [quantum circuit](https://en.wikipedia.org/wiki/Quantum_circuit). A basic workflow using Qiskit consists of two stages: **Build** and **Execute**. **Build** allows you to make different quantum circuits that represent the problem you are solving, and **Execute** that allows you to run them on different backends.  After the jobs have been run, the data is collected and postprocessed depending on the desired output."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-10T11:37:44.387267Z",
     "start_time": "2019-08-10T11:37:41.934365Z"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from qiskit import *\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Circuit Basics <a name='basics'></a>\n",
    "\n",
    "\n",
    "### Building the circuit\n",
    "\n",
    "The basic element needed for your first program is the QuantumCircuit.  We begin by creating a `QuantumCircuit` comprised of three qubits."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-10T11:37:44.392806Z",
     "start_time": "2019-08-10T11:37:44.389673Z"
    }
   },
   "outputs": [],
   "source": [
    "# Create a Quantum Circuit acting on a quantum register of three qubits\n",
    "circ = QuantumCircuit(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After you create the circuit with its registers, you can add gates (\"operations\") to manipulate the registers. As you proceed through the tutorials you will find more gates and circuits; below is an example of a quantum circuit that makes a three-qubit GHZ state\n",
    "\n",
    "$$|\\psi\\rangle = \\left(|000\\rangle+|111\\rangle\\right)/\\sqrt{2}.$$\n",
    "\n",
    "To create such a state, we start with a three-qubit quantum register. By default, each qubit in the register is initialized to $|0\\rangle$. To make the GHZ state, we apply the following gates:\n",
    "- A Hadamard gate $H$ on qubit 0, which puts it into the superposition state $\\left(|0\\rangle+|1\\rangle\\right)/\\sqrt{2}$.\n",
    "- A controlled-Not operation ($C_{X}$) between qubit 0 and qubit 1.\n",
    "- A controlled-Not operation between qubit 0 and qubit 2.\n",
    "\n",
    "On an ideal quantum computer, the state produced by running this circuit would be the GHZ state above.\n",
    "\n",
    "In Qiskit, operations can be added to the circuit one by one, as shown below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-10T11:37:44.401502Z",
     "start_time": "2019-08-10T11:37:44.395545Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<qiskit.circuit.instructionset.InstructionSet at 0x2452282f708>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Add a H gate on qubit 0, putting this qubit in superposition.\n",
    "circ.h(0)\n",
    "# Add a CX (CNOT) gate on control qubit 0 and target qubit 1, putting\n",
    "# the qubits in a Bell state.\n",
    "circ.cx(0, 1)\n",
    "# Add a CX (CNOT) gate on control qubit 0 and target qubit 2, putting\n",
    "# the qubits in a GHZ state.\n",
    "circ.cx(0, 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualize Circuit <a name='visualize'></a>\n",
    "\n",
    "You can visualize your circuit using Qiskit `QuantumCircuit.draw()`, which plots the circuit in the form found in many textbooks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-10T11:37:44.762773Z",
     "start_time": "2019-08-10T11:37:44.403727Z"
    },
    "scrolled": true,
    "tags": [
     "nbsphinx-thumbnail"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 358.792x204.68 with 1 Axes>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "circ.draw('mpl')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this circuit, the qubits are put in order, with qubit zero at the top and qubit two at the bottom. The circuit is read left to right (meaning that gates that are applied earlier in the circuit show up further to the left)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-info\">\n",
    "\n",
    "\n",
    "When representing the state of a multi-qubit system, the tensor order used in Qiskit is different than that used in most physics textbooks. Suppose there are $n$ qubits, and qubit $j$ is labeled as $Q_{j}$. Qiskit uses an ordering in which the $n^{\\mathrm{th}}$ qubit is on the <em><strong>left</strong></em> side of the tensor product, so that the basis vectors are labeled as  $Q_{n-1}\\otimes \\cdots  \\otimes  Q_1\\otimes Q_0$.\n",
    "\n",
    "For example, if qubit zero is in state 0, qubit 1 is in state 0, and qubit 2 is in state 1, Qiskit would represent this state as $|100\\rangle$, whereas many physics textbooks would represent it as $|001\\rangle$.\n",
    "\n",
    "This difference in labeling affects the way multi-qubit operations are represented as matrices. For example, Qiskit represents a controlled-X ($C_{X}$) operation with qubit 0 being the control and qubit 1 being the target as\n",
    "\n",
    "$$C_X = \\begin{pmatrix} 1 & 0 & 0 & 0 \\\\  0 & 0 & 0 & 1 \\\\ 0 & 0 & 1 & 0 \\\\ 0 & 1 & 0 & 0 \\\\\\end{pmatrix}.$$\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Simulating circuits using Qiskit Aer <a name='simulation'></a>\n",
    "\n",
    "Qiskit Aer is our package for simulating quantum circuits. It provides many different backends for doing a simulation. There is also a basic, Python only, implementation called `BasicAer` in Terra that can be used as a drop-in replacement for `Aer` in the examples below.\n",
    "\n",
    "### Statevector backend\n",
    "\n",
    "The most common backend in Qiskit Aer is the `statevector_simulator`. This simulator returns the quantum \n",
    "state, which is a complex vector of dimensions $2^n$, where $n$ is the number of qubits \n",
    "(so be careful using this as it will quickly get too large to run on your machine)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To run the above circuit using the statevector simulator, first you need to import Aer and then set the backend to `statevector_simulator`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-10T11:37:44.768493Z",
     "start_time": "2019-08-10T11:37:44.765280Z"
    }
   },
   "outputs": [],
   "source": [
    "# Import Aer\n",
    "from qiskit import Aer\n",
    "\n",
    "# Run the quantum circuit on a statevector simulator backend\n",
    "backend = Aer.get_backend('statevector_simulator')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have chosen the backend, it's time to compile and run the quantum circuit. In Qiskit we provide the `execute` function for this. ``execute`` returns a ``job`` object that encapsulates information about the job submitted to the backend.\n",
    "\n",
    "\n",
    "<div class=\"alert alert-block alert-info\">\n",
    "<b>Tip:</b> You can obtain the above parameters in Jupyter. Simply place the text cursor on a function and press Shift+Tab.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-10T11:37:44.799134Z",
     "start_time": "2019-08-10T11:37:44.770995Z"
    }
   },
   "outputs": [],
   "source": [
    "# Create a Quantum Program for execution \n",
    "job = execute(circ, backend)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When you run a program, a job object is made that has the following two useful methods: \n",
    "`job.status()` and `job.result()`, which return the status of the job and a result object, respectively.\n",
    "\n",
    "<div class=\"alert alert-block alert-info\">\n",
    "<b>Note:</b> Jobs run asynchronously, but when the result method is called, it switches to synchronous and waits for it to finish before moving on to another task.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-10T11:37:44.804647Z",
     "start_time": "2019-08-10T11:37:44.801681Z"
    }
   },
   "outputs": [],
   "source": [
    "result = job.result()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The results object contains the data and Qiskit provides the method \n",
    "`result.get_statevector(circ)` to return the state vector for the quantum circuit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-10T11:37:44.814182Z",
     "start_time": "2019-08-10T11:37:44.808905Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.707+0.j 0.   +0.j 0.   +0.j 0.   +0.j 0.   +0.j 0.   +0.j 0.   +0.j\n",
      " 0.707+0.j]\n"
     ]
    }
   ],
   "source": [
    "outputstate = result.get_statevector(circ, decimals=3)\n",
    "print(outputstate)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Qiskit also provides a visualization toolbox to allow you to view these results.\n",
    "\n",
    "Below, we use the visualization function to plot the real and imaginary components of the state density matrix $\\rho$.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-10T11:37:45.605645Z",
     "start_time": "2019-08-10T11:37:44.817291Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from qiskit.visualization import plot_state_city\n",
    "plot_state_city(outputstate)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Unitary backend"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Qiskit Aer also includes a `unitary_simulator` that works _provided all the elements in the circuit are unitary operations_. This backend calculates the $2^n \\times 2^n$ matrix representing the gates in the quantum circuit. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-10T11:37:45.626148Z",
     "start_time": "2019-08-10T11:37:45.607840Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.707+0.j  0.707-0.j  0.   +0.j  0.   +0.j  0.   +0.j  0.   +0.j\n",
      "   0.   +0.j  0.   +0.j]\n",
      " [ 0.   +0.j  0.   +0.j  0.   +0.j  0.   +0.j  0.   +0.j  0.   +0.j\n",
      "   0.707+0.j -0.707+0.j]\n",
      " [ 0.   +0.j  0.   +0.j  0.707+0.j  0.707-0.j  0.   +0.j  0.   +0.j\n",
      "   0.   +0.j  0.   +0.j]\n",
      " [ 0.   +0.j  0.   +0.j  0.   +0.j  0.   +0.j  0.707+0.j -0.707+0.j\n",
      "   0.   +0.j  0.   +0.j]\n",
      " [ 0.   +0.j  0.   +0.j  0.   +0.j  0.   +0.j  0.707+0.j  0.707-0.j\n",
      "   0.   +0.j  0.   +0.j]\n",
      " [ 0.   +0.j  0.   +0.j  0.707+0.j -0.707+0.j  0.   +0.j  0.   +0.j\n",
      "   0.   +0.j  0.   +0.j]\n",
      " [ 0.   +0.j  0.   +0.j  0.   +0.j  0.   +0.j  0.   +0.j  0.   +0.j\n",
      "   0.707+0.j  0.707-0.j]\n",
      " [ 0.707+0.j -0.707+0.j  0.   +0.j  0.   +0.j  0.   +0.j  0.   +0.j\n",
      "   0.   +0.j  0.   +0.j]]\n"
     ]
    }
   ],
   "source": [
    "# Run the quantum circuit on a unitary simulator backend\n",
    "backend = Aer.get_backend('unitary_simulator')\n",
    "job = execute(circ, backend)\n",
    "result = job.result()\n",
    "\n",
    "# Show the results\n",
    "print(result.get_unitary(circ, decimals=3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### OpenQASM backend"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The simulators above are useful because they provide information about the state output by the ideal circuit and the matrix representation of the circuit. However, a real experiment terminates by _measuring_ each qubit (usually in the computational $|0\\rangle, |1\\rangle$ basis). Without measurement, we cannot gain information about the state. Measurements cause the quantum system to collapse into classical bits. \n",
    "\n",
    "For example, suppose we make independent measurements on each qubit of the three-qubit GHZ state\n",
    "\n",
    "$$|\\psi\\rangle = (|000\\rangle +|111\\rangle)/\\sqrt{2},$$\n",
    "\n",
    "and let $xyz$ denote the bitstring that results. Recall that, under the qubit labeling used by Qiskit, $x$ would correspond to the outcome on qubit 2, $y$ to the outcome on qubit 1, and $z$ to the outcome on qubit 0. \n",
    "\n",
    "<div class=\"alert alert-block alert-info\">\n",
    "<b>Note:</b> This representation of the bitstring puts the most significant bit (MSB) on the left, and the least significant bit (LSB) on the right. This is the standard ordering of binary bitstrings. We order the qubits in the same way (qubit representing the MSB has index 0), which is why Qiskit uses a non-standard tensor product order.\n",
    "</div>\n",
    "\n",
    "Recall the probability of obtaining outcome $xyz$ is given by\n",
    "\n",
    "$$\\mathrm{Pr}(xyz) = |\\langle xyz | \\psi \\rangle |^{2}$$\n",
    "\n",
    "and as such for the GHZ state probability of obtaining 000 or 111 are both 1/2.\n",
    "\n",
    "To simulate a circuit that includes measurement, we need to add measurements to the original circuit above, and use a different Aer backend."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-10T11:37:45.840681Z",
     "start_time": "2019-08-10T11:37:45.627937Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"word-wrap: normal;white-space: pre;background: #fff0;line-height: 1.1;font-family: &quot;Courier New&quot;,Courier,monospace\">        ┌───┐           ░ ┌─┐      \n",
       "q_0: |0>┤ H ├──■────■───░─┤M├──────\n",
       "        └───┘┌─┴─┐  │   ░ └╥┘┌─┐   \n",
       "q_1: |0>─────┤ X ├──┼───░──╫─┤M├───\n",
       "             └───┘┌─┴─┐ ░  ║ └╥┘┌─┐\n",
       "q_2: |0>──────────┤ X ├─░──╫──╫─┤M├\n",
       "                  └───┘ ░  ║  ║ └╥┘\n",
       " c_0: 0 ═══════════════════╩══╬══╬═\n",
       "                              ║  ║ \n",
       " c_1: 0 ══════════════════════╩══╬═\n",
       "                                 ║ \n",
       " c_2: 0 ═════════════════════════╩═\n",
       "                                   </pre>"
      ],
      "text/plain": [
       "<qiskit.visualization.text.TextDrawing at 0x24522e135c8>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a Quantum Circuit\n",
    "meas = QuantumCircuit(3, 3)\n",
    "meas.barrier(range(3))\n",
    "# map the quantum measurement to the classical bits\n",
    "meas.measure(range(3), range(3))\n",
    "\n",
    "# The Qiskit circuit object supports composition using\n",
    "# the addition operator.\n",
    "qc = circ + meas\n",
    "\n",
    "#drawing the circuit\n",
    "qc.draw()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This circuit adds a classical register, and three measurements that are used to map the outcome of qubits to the classical bits. \n",
    "\n",
    "To simulate this circuit, we use the ``qasm_simulator`` in Qiskit Aer. Each run of this circuit will yield either the bitstring 000 or 111. To build up statistics about the distribution of the bitstrings (to, e.g., estimate $\\mathrm{Pr}(000)$), we need to repeat the circuit many times. The number of times the circuit is repeated can be specified in the ``execute`` function, via the ``shots`` keyword."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-10T11:37:45.868074Z",
     "start_time": "2019-08-10T11:37:45.842666Z"
    }
   },
   "outputs": [],
   "source": [
    "# Use Aer's qasm_simulator\n",
    "backend_sim = Aer.get_backend('qasm_simulator')\n",
    "\n",
    "# Execute the circuit on the qasm simulator.\n",
    "# We've set the number of repeats of the circuit\n",
    "# to be 1024, which is the default.\n",
    "job_sim = execute(qc, backend_sim, shots=1024)\n",
    "\n",
    "# Grab the results from the job.\n",
    "result_sim = job_sim.result()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once you have a result object, you can access the counts via the function `get_counts(circuit)`. This gives you the _aggregated_ binary outcomes of the circuit you submitted."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-10T11:37:45.873600Z",
     "start_time": "2019-08-10T11:37:45.869929Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'000': 510, '111': 514}\n"
     ]
    }
   ],
   "source": [
    "counts = result_sim.get_counts(qc)\n",
    "print(counts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Approximately 50 percent of the time, the output bitstring is 000. Qiskit also provides a function `plot_histogram`, which allows you to view the outcomes. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-10T11:37:45.991815Z",
     "start_time": "2019-08-10T11:37:45.875518Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from qiskit.visualization import plot_histogram\n",
    "plot_histogram(counts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The estimated outcome probabilities $\\mathrm{Pr}(000)$ and  $\\mathrm{Pr}(111)$ are computed by taking the aggregate counts and dividing by the number of shots (times the circuit was repeated). Try changing the ``shots`` keyword in the ``execute`` function and see how the estimated probabilities change."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-08-10T11:38:18.277518Z",
     "start_time": "2019-08-10T11:38:18.224481Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<h3>Version Information</h3><table><tr><th>Qiskit Software</th><th>Version</th></tr><tr><td>Qiskit</td><td>0.14.0</td></tr><tr><td>Terra</td><td>0.11.0</td></tr><tr><td>Aer</td><td>0.3.4</td></tr><tr><td>Ignis</td><td>0.2.0</td></tr><tr><td>Aqua</td><td>0.6.1</td></tr><tr><td>IBM Q Provider</td><td>0.4.4</td></tr><tr><th>System information</th></tr><tr><td>Python</td><td>3.7.4 (default, Aug  9 2019, 18:34:13) [MSC v.1915 64 bit (AMD64)]</td></tr><tr><td>OS</td><td>Windows</td></tr><tr><td>CPUs</td><td>2</td></tr><tr><td>Memory (Gb)</td><td>7.9987335205078125</td></tr><tr><td colspan='2'>Tue Dec 10 15:25:10 2019 Eastern Standard Time</td></tr></table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div style='width: 100%; background-color:#d5d9e0;padding-left: 10px; padding-bottom: 10px; padding-right: 10px; padding-top: 5px'><h3>This code is a part of Qiskit</h3><p>&copy; Copyright IBM 2017, 2019.</p><p>This code is licensed under the Apache License, Version 2.0. You may<br>obtain a copy of this license in the LICENSE.txt file in the root directory<br> of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.<p>Any modifications or derivative works of this code must retain this<br>copyright notice, and modified files need to carry a notice indicating<br>that they have been altered from the originals.</p></div>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import qiskit.tools.jupyter\n",
    "%qiskit_version_table\n",
    "%qiskit_copyright"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "celltoolbar": "Tags",
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
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  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
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   "file_extension": ".py",
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     "delete_cmd_postfix": ") ",
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     "varRefreshCmd": "cat(var_dic_list()) "
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   "types_to_exclude": [
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